我想學(xué)習(xí)人工智能和機(jī)器學(xué)習(xí),我可以從哪里開始呢（二）
I want to learn artificial intelligence and machine learning. Where can I start?
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網(wǎng)友：人們應(yīng)該如何開始學(xué)習(xí)人工智能和機(jī)器學(xué)習(xí)？人工智能是主要領(lǐng)域，而機(jī)器學(xué)習(xí)是子領(lǐng)域。因此，對(duì)于人工智能，我建議你去 麻省理工學(xué)院的公開課網(wǎng)站上看看......
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I want to learn artificial intelligence and machine learning. Where can I start?
我想學(xué)習(xí)人工智能和機(jī)器學(xué)習(xí),我可以從哪里開始呢?
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How should one start learning about AI and machine learning?
AI is main field,while ML is subfield of AI.
So for AI , I would suggest to go with MIT course on their opencourseware site. You can also check udacity's nanodegree or Udacity's Artificial Intelligence course taught by Sebastian Tharun(sorry if I misspelled it). Besides that you can read books on AI. Artificial Intelligence :a modern approach, is a great book. Many people recommend it.
人們應(yīng)該如何開始學(xué)習(xí)人工智能和機(jī)器學(xué)習(xí)？
人工智能是主要領(lǐng)域，而機(jī)器學(xué)習(xí)是子領(lǐng)域。
因此，對(duì)于人工智能，我建議你去 麻省理工學(xué)院的公開課網(wǎng)站上看看。你也可以查看塞巴斯蒂安·塞倫（Sebastian Tharun）教授的在優(yōu)達(dá)學(xué)城平臺(tái)上的納米學(xué)位或人工智能課程。此外，你還可以閱讀有關(guān)人工智能的書籍?！度斯ぶ悄埽含F(xiàn)代方法》是一本很棒的書，很多人都推薦它。
Start with Coursera’s machine learning course by Stanford university. It's really great place to start for beginners in ML. It will teachs you everything necessary to know about ML. After that learn how to use python for machine learning. Make some project using it because coding is only thing that matters a lot in ML or AI. If you want to learn mathematical proofs and want to dive into maths behind ML then go with YouTube serie of video lectures by Stanford. Note they are not for beginners.
Beside it I would suggest to read lot of blogs on net. Use people's code to see how to implement algorithms. Learn how to code good AI,ML models and how to uate algorithms.
After 3 months in ML, start to complete on Kaggle. It's really good place to learn new tricks in ML. Read blogs and interviews of grandmasters on Kaggle, it will increase your knowledge as well as confidence.
Hope it helps.
Happy learning.
對(duì)于機(jī)器學(xué)習(xí)
從斯坦福大學(xué)的Coursera平臺(tái)開始學(xué)習(xí)機(jī)器學(xué)習(xí)課程。對(duì)于機(jī)器學(xué)習(xí)方面初學(xué)者來(lái)說(shuō)，這真的是一個(gè)很好的起點(diǎn)。它將教你了解機(jī)器學(xué)習(xí)的所有必要知識(shí)。之后，學(xué)習(xí)如何使用python進(jìn)行機(jī)器學(xué)習(xí)。使用它制作一些項(xiàng)目，因?yàn)榫幋a是ML或AI中唯一重要的事情。如果你想學(xué)習(xí)數(shù)學(xué)證明，想深入學(xué)習(xí)ML背后的數(shù)學(xué)，那么就去看斯坦福大學(xué)的油管系列視頻講座。請(qǐng)注意，它們不適合初學(xué)者。
除此之外，我還建議在網(wǎng)上讀很多博客。使用人們的代碼來(lái)了解如何實(shí)現(xiàn)算法。學(xué)習(xí)如何編寫好的AI、ML模型以及如何評(píng)估算法。
學(xué)習(xí)機(jī)器語(yǔ)言3個(gè)月后，開始在Kaggle平臺(tái)上完成學(xué)業(yè)。這是學(xué)習(xí)ML新技巧的好地方。閱讀Kaggle平臺(tái)上的博客和大師訪談，這將增加你的知識(shí)和信心。
希望有幫助。
快樂學(xué)習(xí)哈。
Artificial Intelligence (AI) and Machine Learning will be here for the long term. The majority of industry verticals harness AI and ML to create multiple job opportunities and a better future.
Recent innovations such as intelligent voice assistants and self-driving vehicles, robotic process automation, and so on have helped ML and AI gain traction. This has taken the world by storm, and everyone is eager to learn more about it.
AI and ML are in constant change and evolving daily. A few universities offer a formal degree, but you can achieve it in many other ways.
I have compiled the most effective ways to learn AI/Machine Learning after extensive research.
My best tip for learning AI and machine learning is to follow a 5-step process.
人工智能（AI）和機(jī)器學(xué)習(xí)（ML）將長(zhǎng)期存在。大多數(shù)垂直行業(yè)利用AI和ML創(chuàng)造多個(gè)就業(yè)機(jī)會(huì)和更好的未來(lái)。
最近的創(chuàng)新，如智能語(yǔ)音助手和自動(dòng)駕駛車輛、機(jī)器人流程自動(dòng)化等，幫助ML和AI獲得了吸引力。這場(chǎng)風(fēng)暴席卷了世界，每個(gè)人都渴望了解更多。
AI和ML每天都在不斷變化和發(fā)展。一些大學(xué)提供正式學(xué)位，但你可以通過許多其他方式獲得。
經(jīng)過廣泛的研究，我整理了學(xué)習(xí)AI/ML的最有效方法。
我學(xué)習(xí)人工智能和機(jī)器學(xué)習(xí)的最佳建議是遵循5步流程：
原創(chuàng)翻譯：龍騰網(wǎng) http://www.top-shui.cn 轉(zhuǎn)載請(qǐng)注明出處
Change your mindset. You can apply machine learning and AI to your everyday life.
Question yourself, What is Holding you Back From Your Machine Learning Goals?
Why Machine Learning and AI Does Not Have to Be So Hard
How to Think About AI and Machine Learning
Find Your Machine Learning and AI Tribe
Step 2:
Choose a process and use a systemic approach to problem-solving. To solve problems, use a systematic approach.
Applied Machine Learning Process
步驟1：
改變你的心態(tài)。你可以將機(jī)器學(xué)習(xí)和人工智能應(yīng)用到你的日常生活中。
捫心自問，是什么阻礙了你實(shí)現(xiàn)機(jī)器學(xué)習(xí)目標(biāo)？
為什么機(jī)器學(xué)習(xí)和人工智能不必如此困難
如何思考人工智能和機(jī)器學(xué)習(xí)
找到一大群人一起學(xué)習(xí)機(jī)器學(xué)習(xí)和人工智能
第2步：
選擇一個(gè)過程并使用系統(tǒng)的方法解決問題。要解決問題，請(qǐng)使用系統(tǒng)的方法。
應(yīng)用機(jī)器學(xué)習(xí)過程
Choose a tool. Choose a tool that is appropriate for you and map it onto the process.
Beginners: Weka Workbench.
Intermediate: Python Ecosystem.
Advanced: R Platform.
sext the Best Programming Language for Machine Learning and AI that helps you at any level.
Step 4:
Use datasets to your advantage and practice the process. Choose datasets that you would like to use and then practice the process.
Practice Machine Learning with Small In-Memory Datasets
Tour of Real-World AI and Machine Learning Problems
Work on AI and Machine Learning Problems That Matter To You
第3步：
選擇一個(gè)工具。選擇適合你的工具，并將其映射到流程中。
初學(xué)者：Weka Workbench平臺(tái)。
中級(jí)：Python生態(tài)系統(tǒng)。
高級(jí)：R平臺(tái)。
選擇機(jī)器學(xué)習(xí)和人工智能的最佳編程語(yǔ)言，可以在任何水平上幫助到你。
第4步：
根據(jù)自己的優(yōu)勢(shì)使用數(shù)據(jù)集，并練習(xí)該過程。選擇要使用的數(shù)據(jù)集，然后練習(xí)該過程。
使用小內(nèi)存數(shù)據(jù)集練習(xí)機(jī)器學(xué)習(xí)
處理現(xiàn)實(shí)世界的人工智能和機(jī)器學(xué)習(xí)方面難題之旅
研究對(duì)你至關(guān)重要的人工智能和機(jī)器學(xué)習(xí)方面的難題
Get Paid by Applying for an appropriate job
Start and maintain a good career with great achievements and make AI and Machine Learning For Money.
You can learn through E-books.
The best and most traditional way to learn about any field is through books, especially AI and ML.
Many e-books are available such as Artificial Intelligence: A Modern Approach. However, if you want to learn how to create AI, this book is a must-read. The book was written by AI experts Stuart Russell, Peter Norvig. This book covers all aspects of Artificial Intelligence from A to Z, including first-order logic, reinforcement and learning, and neural networks.
Possible with Blogs and Websites
Some many blogs and websites deal with Data Science. Blogs and websites are one of the best learning tools. They also provide many practical skills and experience.
Kdnuggets and Kaggle are some of the most visited blogs and websites. Reddit and Google News on Data Science are two examples of important news sources related to data science.
You can even learn AI and Machine learning through the online platforms
Online courses are the best way to learn AI.
第5步：
通過申請(qǐng)合適的工作獲得報(bào)酬
開始并保持良好的職業(yè)生涯，取得巨大的成就，讓人工智能和機(jī)器學(xué)習(xí)有利可圖。
你可以通過電子書學(xué)習(xí)。
了解任何領(lǐng)域的最好和最傳統(tǒng)的方法是通過書籍，尤其是人工智能和機(jī)器學(xué)習(xí)。
有許多電子書都不錯(cuò)，比如《人工智能:現(xiàn)代方法》。然而，如果你想學(xué)習(xí)如何創(chuàng)建人工智能，這本書是必讀的。這本書由人工智能專家斯圖亞特·羅素(Stuart Russell)、彼得·諾維格(Peter Norvig)撰寫。本書涵蓋了人工智能從A到Z的各個(gè)方面，包括一階邏輯、強(qiáng)化和學(xué)習(xí)以及神經(jīng)網(wǎng)絡(luò)系統(tǒng)。
可以使用博客和網(wǎng)站
一些博客和網(wǎng)站處理數(shù)據(jù)科學(xué)。博客和網(wǎng)站是最好的學(xué)習(xí)工具之一。他們還提供了許多實(shí)用技能和經(jīng)驗(yàn)。
Kdnuggets和Kaggle平臺(tái)是訪問量最大的博客和網(wǎng)站。紅迪網(wǎng)和谷歌數(shù)據(jù)科學(xué)新聞是與數(shù)據(jù)科學(xué)相關(guān)的重要新聞來(lái)源的兩個(gè)例子。
你甚至可以通過在線平臺(tái)學(xué)習(xí)人工智能和機(jī)器學(xué)習(xí)
在線課程是學(xué)習(xí)人工智能的最佳方式。
When you first begin to consider learning machine learning (ML), you should ask yourself a few questions.
What motivates you to pursue it?
Are you a student who has ML in his curriculum? Who aspires to be a data scientist. Are you already working and wish to switch to data science as a career? Are you a business leader who wants to understand ML for their business innovations?
2. How are you going to leverage this knowledge?
Students might use this knowledge to gain a deeper understanding of this area. Already working professionals from different disciplines aspire to add ML to their skill set. Business leaders can leverage this knowledge for the adoption of AI for various business processes.
當(dāng)你開始考慮學(xué)習(xí)機(jī)器學(xué)習(xí)（ML）時(shí)，你應(yīng)該問自己幾個(gè)問題。
是什么激勵(lì)你去追求它？
你是一個(gè)課程體系中有機(jī)器學(xué)習(xí)這門課的學(xué)生嗎？是渴望成為一名數(shù)據(jù)科學(xué)家？你是否已經(jīng)在工作并希望轉(zhuǎn)行從事數(shù)據(jù)科學(xué)？你是想了解機(jī)器學(xué)習(xí)的商業(yè)創(chuàng)新的商業(yè)領(lǐng)袖嗎？
2.你將如何利用這些知識(shí)？
學(xué)生可以利用這些知識(shí)來(lái)加深對(duì)這一領(lǐng)域的理解。來(lái)自不同學(xué)科的在職專業(yè)人士渴望將機(jī)器學(xué)習(xí)添加到他們的技能中。商業(yè)領(lǐng)袖可以利用這些知識(shí)將人工智能應(yīng)用于各種商業(yè)流程。
There is no predefined skill that one must possess for building an understanding of ML. It is not required of a beginner to know everything right away. You don't need to have any prior programming experience or knowledge of coding. A background in computer science may be beneficial, but it is not an essential need. Anyone who wants to learn Machine Learning can start from the beginning, but it's crucial to know their obxtive.
4. What degree of machine learning knowledge is enough for you?
A student who has an obxtive to be a data scientist must have a fundamental knowledge of machine learning to be part of a data-driven future. Professionals from engineering, data science, IT, or any other discipline have to dig deeper. He must learn mathematics and programming. A business executive who wants to implement machine learning in his company doesn't have to dig too far. Business leaders need machine learning expertise to decide if it can solve a challenge for their firm. So, he must have application-level knowledge.
3.學(xué)習(xí)機(jī)器學(xué)習(xí)的先決條件是什么水平的技能？
對(duì)于建立對(duì)機(jī)器學(xué)習(xí)的理解，沒有預(yù)先定義的技能是必須具備的。初學(xué)者不需要馬上知道所有事情。您不需要有任何編程經(jīng)驗(yàn)或編碼知識(shí)。計(jì)算機(jī)科學(xué)背景可能是有益的，但不是必要的。任何想學(xué)習(xí)機(jī)器學(xué)習(xí)的人都可以從頭開始，但了解他們的目標(biāo)至關(guān)重要。
4.什么程度的機(jī)器學(xué)習(xí)知識(shí)對(duì)你來(lái)說(shuō)足夠？
目標(biāo)是成為數(shù)據(jù)科學(xué)家的學(xué)生必須具備機(jī)器學(xué)習(xí)的基本知識(shí)，才能成為數(shù)據(jù)驅(qū)動(dòng)未來(lái)的一部分。來(lái)自工程、數(shù)據(jù)科學(xué)、IT或任何其他學(xué)科的專業(yè)人士都必須深入挖掘。他必須學(xué)習(xí)數(shù)學(xué)和編程。想要在公司中實(shí)現(xiàn)機(jī)器學(xué)習(xí)的業(yè)務(wù)主管不必太過深挖。商業(yè)領(lǐng)袖需要機(jī)器學(xué)習(xí)專業(yè)知識(shí)來(lái)決定它是否能為他們的公司解決挑戰(zhàn)。因此，他必須具備應(yīng)用程序級(jí)的知識(shí)。
原創(chuàng)翻譯：龍騰網(wǎng) http://www.top-shui.cn 轉(zhuǎn)載請(qǐng)注明出處
Working Professionals must have a deep understanding of machine learning. To advance in machine learning, you'll need a diverse range of skills. This is not something that can be done by watching YouTube tutorials. It's more like laying a foundation, and the first step is to get a solid understanding. Begin by taking classes on various online platforms, such as Stanford University's Machine Learning course on Coursera. Otherwise, numerous training institutes provide ML courses.
Students must have a comprehensive understanding. That can be ascertained by attending classroom programs and taking courses that deal with fundamentals.
For business executives, application-level knowledge is sufficient; he does not need to go through complicated structures but only the fundamentals to see how it might benefit his business. He should have comprehensibility where he can leverage the same for innovations. His learning can be accomplished through practical implementations to see how ML can be leveraged for different scenarios. Reading case studies can be of great help.
Disclaimer: I work at Datoin, where one of our core visions is to educate business executives about the relevance of machine learning in today's world and how they can use it to their advantage. Datoin is working on ML-Game, soon to be released, to assist business leaders in comprehending machine learning.
5.選擇前進(jìn)道路：
工作專業(yè)人士必須對(duì)機(jī)器學(xué)習(xí)有深刻的理解。要在機(jī)器學(xué)習(xí)方面取得進(jìn)步，你需要多種技能。這不是通過觀看油管教程就能做到的。這更像是打下基礎(chǔ)，而第一步是獲得堅(jiān)實(shí)的理解。首先在各種在線平臺(tái)上上課，比如斯坦福大學(xué)的Coursera平臺(tái)學(xué)習(xí)機(jī)器學(xué)習(xí)課程。否則，許多培訓(xùn)機(jī)構(gòu)都會(huì)提供機(jī)器學(xué)習(xí)課程。
學(xué)生必須有全面的理解。這可以通過參加課堂課程和參加基礎(chǔ)課程來(lái)確定。
對(duì)于業(yè)務(wù)主管來(lái)說(shuō)，應(yīng)用程序級(jí)知識(shí)就足夠了；他不需要經(jīng)歷復(fù)雜的結(jié)構(gòu)，只需要了解基本原理，就可以看到這對(duì)他的業(yè)務(wù)有什么好處。他應(yīng)該具有可理解性，以便能夠利用這一點(diǎn)進(jìn)行創(chuàng)新。他的學(xué)習(xí)可以通過實(shí)際實(shí)現(xiàn)來(lái)完成，以了解如何在不同的場(chǎng)景中利用機(jī)器學(xué)習(xí)。閱讀案例研究會(huì)有很大幫助。
免責(zé)聲明：我在Datoin工作，在那里我們的核心愿景之一是教育企業(yè)高管，讓他們了解機(jī)器學(xué)習(xí)在當(dāng)今世界的相關(guān)性，以及如何利用機(jī)器學(xué)習(xí)為自己創(chuàng)造優(yōu)勢(shì)。Datoin正在開發(fā)即將發(fā)布的機(jī)器學(xué)習(xí)類游戲 ，以幫助商業(yè)領(lǐng)袖理解機(jī)器學(xué)習(xí)。
Artificial Intelligence is one breakthrough career path of the twenty-first century. It is a remarkable and intelligent decision to choose AI as your career path. People in the IT industry know its potential and pathbreaking career, which they can achieve through learning AI.
This is the reason many IT people are moving towards learning this technology. Apart from that every single industrial domain such as BFSI, Marketing, HR, manufacturing, etc is getting highly dependent on AI innovations.
AI is not just robots shown in movies. It is much more than just that. It is expert-level coding provided by computer systems through processing models to train them to behave in a certain way.
Anyone who wants to learn AI is a beginner at some time, whether it is a fresh college graduate or an individual already working and wants to move into this career path. Everyone starts somewhere someday. So, never take the pressure of the syllabus. For sure, learning is huge, but it is not complex. If followed properly, anyone can achieve it and interest matters more than the discussion on difficulty level.
Let’s go through the AI learning path and what are the pre-requirements before choosing this technology as your career path. I have covered all the important points below, so do give it a read:
人工智能是21世紀(jì)的一條突破性職業(yè)道路。選擇人工智能作為職業(yè)道路是一個(gè)非凡而明智的決定。IT行業(yè)的人知道其是具備潛力和開創(chuàng)性的職業(yè)，他們可以通過學(xué)習(xí)AI來(lái)實(shí)現(xiàn)這一目標(biāo)。
這就是許多IT人員開始學(xué)習(xí)這項(xiàng)技術(shù)的原因。除此之外，每個(gè)工業(yè)領(lǐng)域，如金融服務(wù)和保險(xiǎn)業(yè)、市場(chǎng)營(yíng)銷、人力資源、制造業(yè)等，都高度依賴人工智能方面創(chuàng)新。
人工智能不僅僅是電影中出現(xiàn)的機(jī)器人，它的領(lǐng)域遠(yuǎn)不止于此。它是計(jì)算機(jī)系統(tǒng)通過處理模型提供的專家級(jí)編碼，以訓(xùn)練它們以某種方式行為。
任何想要學(xué)習(xí)AI的人無(wú)論是剛畢業(yè)的大學(xué)畢業(yè)生，還是已經(jīng)開始工作并希望進(jìn)入這一職業(yè)道路的個(gè)人，在某種程度上都是初學(xué)者。每個(gè)人總有一天會(huì)從某個(gè)地方開始。所以，千萬(wàn)不要承受教學(xué)大綱的壓力。當(dāng)然，學(xué)習(xí)的工作量是巨大的，但并不復(fù)雜。如果遵循得當(dāng)，任何人都可以做到，興趣比討論難度更重要。
讓我們先了解一下人工智能學(xué)習(xí)路徑，以及選擇這項(xiàng)技術(shù)作為職業(yè)道路之前的先決條件是什么。我下面已經(jīng)涵蓋了所有要點(diǎn)，所以請(qǐng)閱讀：
AI is a complicated technology that necessitates a thorough understanding of how systems and software operate. The following are some of the reasons why this technology isn't suitable for fresh graduates:
Compared to a total beginner, a working professional who has accumulated several years of knowledge on how systems work and what approaches are utilised in design thinking will find it easier to understand AI terms.
AI entails deciphering backend data and is based on real-time streaming of data generated by various businesses. In actual life, a fresher will be unable to comprehend the data and its complexities.
This skill is extremely useful for those who have experience with coding. A newbie may know how to code, but there's a good chance he or she will waste a lot of time cleaning up the code in the beginning rather than working on algorithms.
學(xué)習(xí)人工智能的先決條件：
人工智能是一項(xiàng)復(fù)雜的技術(shù)，需要徹底了解系統(tǒng)和軟件的運(yùn)行方式。以下是該技術(shù)不適合應(yīng)屆畢業(yè)生的一些原因：
與一個(gè)完全的初學(xué)者相比，一個(gè)在系統(tǒng)如何工作和設(shè)計(jì)思維中使用什么方法方面積累了幾年知識(shí)的專業(yè)人士會(huì)發(fā)現(xiàn)更容易理解AI術(shù)語(yǔ)。
人工智能需要破譯后端數(shù)據(jù)，并基于各種業(yè)務(wù)生成的實(shí)時(shí)數(shù)據(jù)流。在實(shí)際生活中，初學(xué)者將無(wú)法理解數(shù)據(jù)及其復(fù)雜性。
對(duì)于那些有編碼經(jīng)驗(yàn)的人來(lái)說(shuō)，這項(xiàng)技能非常有用。新手可能知道如何編碼，但他或她很有可能會(huì)在一開始就浪費(fèi)大量時(shí)間清理代碼，而不是研究算法。
AI is in high demand, so it's always a good idea to brush up on your skills. So, for someone who wants to learn AI but doesn't have a clear curriculum, I have written a step-by-step guide to learn AI from basic to intermediate levels.
1. Working on your fundamentals: First and foremost, work on your fundamentals before going on to more complex topics in order to begin working with AI.
2. It's a good idea to start with Maths. Brush up on your math skills and go over the following concepts again:
Matrix and Determinants, as well as Linear Algebra.
Calculus is a branch of mathematics that deals with Differentiation and Integration.
Vectors, statistics and Probability, graph theory.
這主要是因?yàn)閺氖氯斯ぶ悄茼?xiàng)目的企業(yè)需要一套專門的技能來(lái)完成高度復(fù)雜、復(fù)雜的問題陳述，而很多人都缺乏這種技能。
人工智能的需求量很大，所以提高你的技能總是一個(gè)好主意。因此，對(duì)于那些想學(xué)習(xí)人工智能但沒有明確課程的人，我寫了一份從基礎(chǔ)到中級(jí)學(xué)習(xí)人工智能的分步指南。
1.學(xué)習(xí)基礎(chǔ)知識(shí)：首先，在學(xué)習(xí)更復(fù)雜的主題之前，先學(xué)習(xí)基礎(chǔ)知識(shí)，以便開始使用人工智能。
2.從數(shù)學(xué)開始是個(gè)好主意。復(fù)習(xí)一下你的數(shù)學(xué)技能，再?gòu)?fù)習(xí)一下以下概念：
矩陣和行列式，以及線性代數(shù)。
微積分是處理微分和積分的數(shù)學(xué)分支。
向量、統(tǒng)計(jì)和概率、圖論。
4. Working on Datasets: Once you have mastered any coding language, you can move on to working with backend components such as databases. For example, you may now use SQL connector or other import modules to connect python or frontend IDE.
5. Lastly, I would suggest that you brush up your skills once you're equipped with all the practice work. You should enrol or join in for some courses to speed up the process. There are various online courses to learn from, but I have listed a few good ones which you can undertake for betterment.
There are some amazing institutes from different sites which I surely want to recommend here:
3.編碼語(yǔ)言：一旦你掌握了算術(shù)技能，你就可以通過選擇一種編碼語(yǔ)言開始練習(xí)編碼?？梢匝芯縅ava或Python。Python是三種語(yǔ)言中最容易學(xué)習(xí)和練習(xí)編碼的，因?yàn)樗懈鞣N各樣的程序包，如Numpy和Panda。
4.處理數(shù)據(jù)集：一旦掌握了任何編碼語(yǔ)言，就可以繼續(xù)處理后端組件，如數(shù)據(jù)庫(kù)。例如，您現(xiàn)在可以使用結(jié)構(gòu)化查詢語(yǔ)言連接器或其他導(dǎo)入模塊連接python或前端成開發(fā)環(huán)境。
5.最后，我建議你在準(zhǔn)備好所有的練習(xí)工作后，提高你的技能。你應(yīng)該注冊(cè)或參加一些課程以加快進(jìn)程。有各種各樣的在線課程可以學(xué)習(xí)，但我列出了一些不錯(cuò)的課程，你可以通過它們來(lái)提高自己。
我想在這里推薦一些來(lái)自不同網(wǎng)站的很棒的學(xué)院:
Variable courses that meet your ultimate needs-
Depending on your profile, you can sext from a variety of options. They provide different courses for techies, non-techies, early pros, intermediate, and even leadership level pros. Their courses are associated with elective modules as per candidates domain expertise. Aspirants can choose elective modules and capstone data science projects expertise as per their choice. Available options include people administration, promotion and salesforce assessment, production and telecom, insurance and finance, leisure and travel, transportation, energy, oil and gas, etc.
Additional project expertise and domain-elective study scopes for techies- The advanced AI and ML course imparted by Learnbay has three additional electives apart from the one mentioned earlier. These are from the core engineering field- Advanced data structure and algorithm, system designing, Embedded engineering, etc.
1.Learnbay平臺(tái)：Learnbay提供基礎(chǔ)、中級(jí)和高級(jí)以及高級(jí)管理層的人工智能課程。如果你有一年以上的工作經(jīng)驗(yàn)，沒有其他平臺(tái)可以像Learnbay一樣出色。
滿足你最終需求的可變課程-
根據(jù)你的個(gè)人資料，可以從多種選項(xiàng)中進(jìn)行選擇。他們?yōu)榧夹g(shù)人員、非技術(shù)人員、早期專業(yè)人員、中級(jí)專業(yè)人員甚至領(lǐng)導(dǎo)層專業(yè)人員提供不同的課程。根據(jù)候選人的專業(yè)知識(shí)，他們的課程與選修模塊相關(guān)聯(lián)。有志者可以根據(jù)自己的選擇選擇選修模塊和頂級(jí)數(shù)據(jù)科學(xué)項(xiàng)目專業(yè)知識(shí)?？捎玫倪x項(xiàng)包括人事管理、晉升和銷售人員評(píng)估、生產(chǎn)和電信、保險(xiǎn)和金融、休閑和旅游、交通、能源、石油和天然氣等。
技術(shù)人員的額外項(xiàng)目專業(yè)知識(shí)和領(lǐng)域選修范圍-Learnbay教授的高級(jí)人工智能和機(jī)器學(xué)習(xí)課程除了前面提到的課程外，還有三個(gè)額外的選修課。這些來(lái)自核心工程領(lǐng)域——高級(jí)數(shù)據(jù)結(jié)構(gòu)和算法、系統(tǒng)設(shè)計(jì)、嵌入式工程等。
The submodules are so well-planned and well-designed that given a bigger find it is easy to learn, although the course covers the most trending and advanced level of industry-specific AI application.
Stacks of the practical assignments with on-time expert feedback-
Starting from the initial programming classes to the advanced ML modelling sessions, you will receive plenty of coding assignments. Experts will provide you with timely feedback on all the submitted assignments so that you can rectify all of your mistakes prior to stepping into the next sessions.
1 to 1 learning support-
Even though you are a beginner, due to extremely personalised teaching, you won't feel the subject hard. You will get proper assistance at every single step of your learning. Apart from that, you get ample flexibility to join the live classes at any time, from any batch.
你從零基礎(chǔ)開始學(xué)習(xí)，直到達(dá)到高級(jí)水平；
雖然課程涵蓋了行業(yè)特定人工智能應(yīng)用的最流行和最先進(jìn)水平，但子模塊的規(guī)劃和設(shè)計(jì)非常好，給定一個(gè)更大的發(fā)現(xiàn)，它很容易學(xué)習(xí)。
成堆的實(shí)踐作業(yè)和及時(shí)的專家反饋
從最初的編程類到高級(jí)機(jī)器學(xué)習(xí)建模會(huì)話，你將收到大量的編碼任務(wù)。專家將為你及時(shí)反饋所有提交的作業(yè)，以便你在進(jìn)入下一個(gè)課程之前糾正所有錯(cuò)誤。
1對(duì)1學(xué)習(xí)支持。
盡管你是一名初學(xué)者，但由于極其個(gè)性化的教學(xué)，你不會(huì)覺得這門課很難。你在學(xué)習(xí)的每一步都會(huì)得到適當(dāng)?shù)膸椭３酥?，你還可以在任何時(shí)間、任何批次加入實(shí)時(shí)課堂。
Lifetime access to learning materials-
The recorded copy of attended live classes, all the premium learning materials, remain free to access for the rest of your life.
They also believe that learning should be done through real-world based projects. As a result, you will gain experience with how AI works in 15+ real-time projects.
2. Edureka: Edureka is already a well-known brand that has been on the market for quite some time. They also feature several AI-specific mini-series to learn from. In addition, you will get lifetime access to their courses once you purchase them.
Learbay平臺(tái)為參加和資格認(rèn)證黑客馬拉松提供指導(dǎo)。這有助于你磨練編碼技能。
終身獲取學(xué)習(xí)材料；
參加現(xiàn)場(chǎng)課程的錄制課件，所有優(yōu)質(zhì)學(xué)習(xí)材料，在你的余生中都可以免費(fèi)使用。
他們還認(rèn)為，學(xué)習(xí)應(yīng)該通過基于現(xiàn)實(shí)世界的項(xiàng)目來(lái)完成。因此，你將獲得人工智能如何在15個(gè)以上實(shí)時(shí)項(xiàng)目中工作的經(jīng)驗(yàn)。
2.Edureka平臺(tái)：Edureka已經(jīng)是一個(gè)知名品牌，在市場(chǎng)上已經(jīng)有一段時(shí)間了。他們還以幾個(gè)人工智能特定的迷你系列為特色進(jìn)行學(xué)習(xí)。此外，一旦你購(gòu)買了他們的課程，你就可以終身訪問他們的課程。
原創(chuàng)翻譯：龍騰網(wǎng) http://www.top-shui.cn 轉(zhuǎn)載請(qǐng)注明出處
4. Coursera: On the internet, it is ranked in the top five. More than 3 lakh students have taken Stanford University ML courses, which has received a 4.9 out of 5. It Covers machine learning like Supervised and unsupervised learning, logistic regression and artificial neural networks, ML algo. These are among the topics covered and are best practises in AI and ML
Finally, I would like to recommend Learnbay for working pros and Edureka for freshers. Coursera is suggested exclusively for those who want to learn just for knowing more, not with a career up-gradation target.
3.Simplilearn平臺(tái)：Simplilearn提供與IBM、AWS、Facebook藍(lán)圖和微軟的關(guān)系，以及一些精彩的課程可供選擇。他們非常強(qiáng)調(diào)結(jié)構(gòu)化的學(xué)習(xí)和現(xiàn)實(shí)挑戰(zhàn)的實(shí)踐。
4.Coursera平臺(tái)：在互聯(lián)網(wǎng)上，它排名前五。超過300萬(wàn)名學(xué)生參加了斯坦福大學(xué)的機(jī)器學(xué)習(xí)課程，在5門課程中獲得了4.9分。該課程涵蓋了機(jī)器學(xué)習(xí)，如監(jiān)督和無(wú)監(jiān)督學(xué)習(xí)、邏輯回歸和人工神經(jīng)網(wǎng)絡(luò)、機(jī)器學(xué)習(xí)算法。這些是涵蓋的主題之一，是人工智能和機(jī)器學(xué)習(xí)的最佳實(shí)踐
最后，我想向職業(yè)人士推薦Learnbay平臺(tái)，向初學(xué)者推薦Edureka平臺(tái)。Coursera平臺(tái)是專為那些只是為了了解更多，而不是為了職業(yè)升級(jí)的目標(biāo)的人準(zhǔn)備的。